System and method for a cooperative conversational voice user interface

ABSTRACT

A cooperative conversational voice user interface is provided. The cooperative conversational voice user interface may build upon short-term and long-term shared knowledge to generate one or more explicit and/or implicit hypotheses about an intent of a user utterance. The hypotheses may be ranked based on varying degrees of certainty, and an adaptive response may be generated for the user. Responses may be worded based on the degrees of certainty and to frame an appropriate domain for a subsequent utterance. In one implementation, misrecognitions may be tolerated, and conversational course may be corrected based on subsequent utterances and/or responses.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/251,712, entitled “System and Method for a Cooperative ConversationalVoice User Interface,” filed Oct. 3, 2011 (which issued as U.S. Pat. No.8,515,765 on Aug. 20, 2013), which is a continuation of U.S. patentapplication Ser. No. 11/580,926, entitled “System and Method for aCooperative Conversational Voice User Interface,” filed Oct. 16, 2006(which issued as U.S. Pat. No. 8,073,681 on Dec. 6, 2011), each of whichare hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The invention relates to a cooperative conversational model for a humanto machine voice user interface.

BACKGROUND OF THE INVENTION

Advances in technology, particularly within the convergence space, haveresulted in an increase in demand for voice recognition software thatcan exploit technology in ways that are intuitive to humans. Whilecommunication between human beings is most often “cooperative,” in thatinformation and/or context is shared to advance mutual conversationalgoals, existing Human-to-Machine interfaces fail to provide the samelevel of intuitive interaction. For example, each human participant in aconversation can contribute to an exchange for the benefit of theexchange. This is done through shared assumptions and expectationsregarding various aspects of the conversation, such as the topic,participant knowledge about the topic, expectations of the otherparticipant's knowledge about the topic, appropriate word usage for thetopic and/or participants, conversational development based on previousutterances, the participants' tone or inflection, the quality andquantity of contribution expected from each participant, and many otherfactors. Participating in conversations that continually build and drawupon shared information is a natural and intuitive way for humans toconverse.

In contrast, complex Human-to-Machine interfaces do not allow users toexploit technology in an intuitive way, which inhibits mass-marketadoption for various technologies. Incorporating a speech interfacehelps to alleviate this burden by making interaction easier and faster,but existing speech interfaces (when they actually work) still requiresignificant learning on the part of the user. That is, existing speechinterfaces are unable to bridge the gap between archaic Human-to-Machineinterfaces and conversational speech that would make interaction withsystems feel normal. Users should be able to directly request what theywant from a system in a normal, conversational fashion, without havingto memorize exact words or phrases. Alternatively, when users areuncertain of particular needs, they should be able to engage the systemin a productive, cooperative dialogue to resolve their requests.Instead, existing speech interfaces force users to dumb down theirrequests to match simple sets of instructions in simple languages inorder to communicate requests in ways that systems can understand. Usingexisting speech interfaces, there is virtually no option for dialoguebetween the user and the system to satisfy mutual goals.

Therefore, existing systems lack a conversational speech model that canprovide users with the ability to interact with systems in ways that areinherently intuitive to human beings. Existing systems suffer from theseand other problems.

SUMMARY OF THE INVENTION

According to various embodiments and aspects of the invention, acooperative conversational voice user interface may understand free formhuman utterances, freeing users from being restricted to a fixed set ofcommands and/or requests. Rather, users can engage in cooperativeconversations with a machine to complete a request or series of requestsusing a natural, intuitive, free form manner of expression.

According to an aspect of the invention, an exemplary systemarchitecture for implementing a cooperative conversational voice userinterface is provided. The system may receive an input, which mayinclude a human utterance received by an input device, where theutterance may include one or more requests. As used herein, an“utterance” may be words, syllables, phonemes, or any other audiblesound made by a human being. As used herein, a “request” may be acommand, directive, or other instruction for a device, computer, orother machine to retrieve information, perform a task, or take someother action. In one implementation, the input may be a multi-modalinput, where at least part of the multi-modal input is an utterance. Theutterance component of the input may be processed by a speechrecognition engine (which may alternatively be referred to as anAutomatic Speech Recognizer or ASR) to generate one or more preliminaryinterpretations of the utterance. The one or more preliminaryinterpretations may then be provided to a conversational speech enginefor further processing, where the conversational speech engine maycommunicate with one or more databases to generate an adaptiveconversational response, which may be returned to the user as an output.In one implementation, the output may be a multi-modal output. Forexample, the utterance may include a request to perform an action, andthe output may include a conversational response reporting success orfailure, as well as an execution of the action.

According to another aspect of the invention, an exemplaryconversational speech engine may generate an adaptive conversationalresponse to a request or series of requests. The conversational speechengine may include a free form voice search module that may understandan utterance made using typical, day-to-day language (i.e., in freeform), and may account for variations in how humans normally speak, thevocabulary they use, and the conditions in which they speak. To accountfor intangible variables of human speech, the free form search modulemay include models of casual human speech. For example, in oneimplementation, the free form search module may understand specializedjargon and/or slang, tolerate variations in word order, and tolerateverbalized pauses or stuttered speech. For example, formalized Englishrequests, where a verb precedes a noun, may be treated in an equivalentmanner to requests where the noun precedes the verb. In anotherimplementation, compound requests and/or compound tasks with multiplevariables may be identified in a single utterance. By identifying allrelevant information for completing one or more tasks from a singleutterance, advantages may be provided over existing voice userinterfaces, such as Command and Control systems that use verbal menus torestrict information that a person can provide at a given point. Inanother implementation, inferring intended requests from incomplete orambiguous requests may provide a conversational feel. By modeling whatcontextual signifiers, qualifiers, or other information may be requiredto perform a task in an identified context, an adaptive response may begenerated, such as prompting a user for missing contextual signifiers,qualifiers, or other information. In one implementation, the responsemay ask for missing information in a way that most restricts possibleinterpretations, and the response may be framed to establish a domainfor a subsequent user utterance. In another implementation, commonalternatives for nouns and verbs may be recognized to reflect variationsin usage patterns according to various criteria. Thus, variations inexpression may be supported because word order is unimportant orunanticipated, and nouns and/or verbs may be represented in differentways to give simplistic, yet representative, examples. In anotherimplementation, requests may be inferred from contradictory or otherwiseinaccurate information, such as when an utterance includes starts andstops, restarts, stutters, run-on sentences, or other imperfect speech.For example, a user may sometimes change their mind, and thus alter therequest in mid-utterance, and the imperfect speech feature maynonetheless be able to infer a request based on models of human speech.For example, various models may indicate that a last criterion is mostlikely to be correct, or intonation, emphasis, stress, use of the word“not,” or other models may indicate which criterion is most likely to becorrect.

According to another aspect of the invention, the conversational speechengine may include a noise tolerance module that may discard words ornoise which has no meaning in a given context to reduce a likelihood ofconfusion. Moreover, the noise tolerance module may filter outenvironmental and non-human noise to further reduce a likelihood ofconfusion. In one implementation, the noise tolerance module maycooperate with other modules and features to filter out words that donot fit into an identified context. For example, the noise tolerancemodule may filter other human conversations and/or utterances within arange of one or more microphones. For example, a single device mayinclude multiple microphones, or multiple devices may each include oneor more microphones, and the noise tolerance module may collate inputsand cooperatively filter out sound by comparing a speech signal from thevarious microphones. The noise tolerance module may also filter outnon-human environmental noise within range of the microphones,out-of-vocabulary words caused by speaker ambiguity or malapropisms, orother noise that may be unrelated to a target request. Performancebenchmarks for the noise tolerance module may be defined by noise modelsbased on human criteria. For example, if a driver of a car is 92% likelyto be understood by a passenger when traveling at 65 miles-per-hour withwindows cracked, then performance benchmarks for the noise tolerancemodule may have a similar performance under such conditions.

According to another aspect of the invention, the conversational speechengine may include a context determination process that determines oneor more contexts for a request to establish meaning within aconversation. The one or more contexts may be determined by having oneor more context domain agents compete to determine a most appropriatedomain for a given utterance. Once a given domain agent “wins” thecompetition, the winning domain agent may be responsible forestablishing or inferring further contexts and updating short-term andlong-term shared knowledge. If there is a deadlock between contextdomain agents, an adaptive conversational response may prompt the userto assist in disambiguating between the deadlocked agents. Moreover, thecontext determination process may infer intended operations and/orcontext based on previous utterances and/or requests, whereas existingsystems consider each utterance independently, potentially making thesame errors over and over again. For example, if a given interpretationturns out to be incorrect, the incorrect interpretation may be removedas a potential interpretation from one or more grammars associated withthe speech recognition engine and/or from possible interpretationsdetermined by the conversational speech engine, thereby assuring that amistake will not be repeated for an identical utterance.

The context determination process may provide advantages over existingvoice user interfaces by continually updating one or more models of anexisting context and establishing context as a by-product of aconversation, which cannot be established a priori. Rather, the contextdetermination process may track conversation topics and attempt to fit acurrent utterance into recent contexts, including switching betweencontexts as tasks are completed, partially completed, requested, etc.The context determination process may identify one or more contextdomains for an utterance by defining a collection of related functionsthat may be useful for users in various context domains. Moreover, eachcontext domain may have relevant vocabularies and thought collections tomodel word groupings, which when evaluated together, may disambiguateone context domain from another. Thus, eliminating out-of-context wordsand noise words when searching for relevant combinations may enhanceaccuracy of inferences. This provides advantages over existing systemsthat attempt to assign meaning to every component of an utterance (i.e.,including out-of-context words and noise words), which results in nearlyinfinite possible combinations and greater likelihood of confusion. Thecontext determination process may also be self-aware, assigning degreesof certainty to one or more generated hypotheses, where a hypothesis maybe developed to account for variations in environmental conditions,speaker ambiguity, accents, or other factors. By identifying a context,capabilities within the context, vocabularies within the context, whattasks are done most often historically in the context, what task wasjust completed, etc., the context determination process may establishintent from rather meager phonetic clues. Moreover, just as inhuman-to-human conversation, users may switch contexts at any timewithout confusion, enabling various context domains to be rapidlyselected, without menu-driven dead ends, when an utterance isunambiguous.

According to another aspect of the invention, an exemplary cooperativeconversational model may build upon free form voice search, noisetolerance, and context determination to implement a conversationalHuman-to-Machine interface that reflects human interaction and normalconversational behavior. That is, the cooperative conversational modelenables humans and machines to participant in a conversation with anaccepted purpose or direction, with each participant contributing to theconversation for the benefit of the conversation. By taking advantage ofhuman presumptions about utterances that humans rely upon, both asspeakers and listeners, a Human-to-Machine interface may be analogous toeveryday human-to-human conversation. In one implementation, theexemplary cooperative conversation model may take incoming data (sharedknowledge) to inform a decision (intelligent hypothesis building), andthen may refine the decision and generate a response (adaptive responsebuilding).

According to another aspect of the invention, shared knowledge mayinclude both short-term and long-term knowledge. Short-term knowledgemay accumulate during a single conversation, where input received duringa single conversation may be retained. The shared knowledge may includecross-modality awareness, where in addition to accumulating inputrelating to user utterances, requests, locations, etc., the sharedknowledge may accumulate a current user interface state relating toother modal inputs to further build shared knowledge models. The sharedknowledge may be used to build one or more intelligent hypotheses usingcurrent and relevant information, build long-term shared knowledge byidentifying information with long-term significance, and generateadaptive responses with relevant state and word usage information.Moreover, because cooperative conversations model human conversations,short-term session data may be expired after a psychologicallyappropriate amount of time, thereby humanizing system behavior, reducinga likelihood of contextual confusion based on stale data, while alsoadding relevant information from an expired session context to long-termknowledge models. Long-term shared knowledge may generally beuser-centric, rather than session-based, where inputs may be accumulatedover time to build user, environmental, cognitive, historical, or otherlong-term knowledge models. Long-term and short-term shared knowledgemay be used simultaneously anytime a user engages in a cooperativeconversation. Long-term shared knowledge may include explicit and/orimplicit user preferences, a history of recent contexts, requests,tasks, etc., user-specific jargon related to vocabularies and/orcapabilities of a context, most often used word choices, or otherinformation. The long-term shared knowledge may be used to build one ormore intelligent hypotheses using current and relevant information,generate adaptive responses with appropriate word choices whenunavailable via short-term shared knowledge, refine long-term sharedknowledge models, identify a frequency of specific tasks, identify tasksa user frequently has difficulty with, or provide other informationand/or analysis to generate more accurate conversational responses.Shared knowledge may also be used to adapt a level of unprompted support(e.g., for novices versus experienced users, users who are frequentlymisrecognized, etc.) Thus, shared knowledge may enable a user and avoice user interface to share assumptions and expectations such as topicknowledge, conversation history, word usage, jargon, tone, or otherassumptions and/or expectations that facilitate a cooperativeconversation between human users and a system.

According to another aspect of the invention, a conversation type may beidentified for any given utterance. Categorizing and developingconceptual models for various types of exchanges may consistently alignuser expectations and domain capabilities. One or more intelligenthypotheses may be generated as to a conversation type by consideringconversational goals, participant roles, and/or an allocation ofinformation among the participants. Based on the conversational goals,participant roles, and allocation of information, the intelligenthypotheses may consider various factors to classify a conversation (orutterance) into general types of conversations that can interact withone another to form many more variations and permutations ofconversation types (e.g., a conversation type may change dynamically asinformation is reallocated from one participant to another, or asconversational goals change based on the reallocation of information).

According to another aspect of the invention, the intelligent hypothesesmay include one or more hypotheses of a user's intent in an utterance.In addition, the intelligent hypotheses may use short-term and/orlong-term shared knowledge to proactively build and evaluate interactionwith a user as a conversation progresses or over time. The hypothesesmay model human-to-human interaction to include a varying degree ofcertainty for each hypothesis. That is, just as humans rely on knowledgeshared by participants to examine how much and what kind of informationwas available, the intelligent hypotheses may leverage the identifiedconversation type and shared knowledge to generate a degree of certaintyfor each hypothesis.

According to another aspect of the invention, syntactically,grammatically, and contextually sensitive “intelligent responses” may begenerated from the intelligent hypotheses that can be used to generate aconversational experience for a user, while also guiding the user toreply in a manner favorable for recognition. The intelligent responsesmay create a conversational feel by adapting to a user's manner ofspeaking, framing responses appropriately, and having natural variationand/or personality (e.g., by varying tone, pace, timing, inflection,word use, jargon, and other variables in a verbal or audible response).

According to another aspect of the invention, the intelligent responsesmay adapt to a user's manner of speaking by using contextual signifiersand grammatical rules to generate one or more sentences that maycooperate with the user. By taking advantage of shared knowledge abouthow a user utters a request, the responses may be modeled using similartechniques used to recognize requests. The intelligent responses mayrate possible responses statistically and/or randomize responses, whichcreates an opportunity to build an exchange with natural variation andconversational feel. This provides advantages over existing voice userinterfaces where input and output is incongruous, as the input is“conversational” and the output is “computerese.”

According to another aspect of the invention, the intelligent responsesmay frame responses to influence a user reply utterance for easyrecognition. For example, the responses may be modeled to illicitutterances from the user that may be more likely to result in acompleted request. Thus, the responses may conform to a cooperativenature of human dialog and a natural human tendency to “parrot” what wasjust heard as part of a next utterance. Moreover, knowledge of currentcontext may enhance responses to generate more meaningful conversationalresponses. Framing the responses may also deal with misrecognitionsaccording to human models. For example, humans frequently remember anumber of recent utterances, especially when one or more previousutterances were misrecognized or unrecognized. Another participant inthe conversation may limit correction to a part of the utterance thatwas misrecognized or unrecognized, or over subsequent utterances and/orother interactions, clues may be provided to indicate the initialinterpretation was incorrect. Thus, by storing and analyzing multipleutterances, utterances from earlier in a conversation may be correctedas the conversation progresses.

According to another aspect of the invention, the intelligent responsesmay include multi-modal, or cross-modal, responses to a user. In oneimplementation, responses may be aware of and control one or moredevices and/or interfaces, and users may respond by using whicheverinput method, or combination of input methods, is most convenient.

According to another aspect of the invention, the intelligent responsesmay correct a course of a conversation without interruptingconversational flow. That is, even though the intelligent responses maybe reasonably “sure,” the intelligent responses may nonethelesssometimes be incorrect. While existing voice user interfaces tend tofail on average conversational missteps, normal human interactions mayexpect missteps and deal with them appropriately. Thus, responses aftermisrecognitions may be modeled after clarifications, rather than errors,and words may chosen in subsequent responses to move conversationforward and establish an appropriate domain to be explored with theuser.

Other objects and advantages of the invention will be apparent to thoseskilled in the art based on the following drawings and detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram of a system architecture accordingto one aspect of the invention.

FIG. 2 is an exemplary block diagram of a conversational speech engineaccording to one aspect of the invention.

FIG. 3 is an exemplary block diagram of a cooperative conversationalmodel according to one aspect of the invention.

DETAILED DESCRIPTION

Referring to FIG. 1, an exemplary system architecture for implementing acooperative conversational voice user interface is illustrated accordingto one aspect of the invention. The system may receive an input 105 froma user, where in one implementation, input 105 may be an utterancereceived by an input device (e.g., a microphone), where the utterancemay include one or more requests. Input 105 may also be a multi-modalinput, where at least part of the multi-modal input is an utterance. Forexample, the input device may include a combination of a microphone anda touch-screen device, and input 105 may include an utterance thatincludes a request relating to a portion of a display on thetouch-screen device that the user is touching. For instance, thetouch-screen device may be a navigation device, and input 105 mayinclude an utterance of “Give me directions to here,” where the user maybe requesting directions to a desired destination on the display of thenavigation device.

The utterance component of input 105 may be processed by a speechrecognition engine 110 (which may alternatively be referred to herein asAutomatic Speech Recognizer 110, or as shown in FIG. 1, ASR 110) togenerate one or more preliminary interpretations of the utterance. Thespeech recognition engine 110 may process the utterance using anysuitable technique known in the art. For example, in one implementation,the speech recognition engine 110 may interpret the utterance usingtechniques of phonetic dictation to recognize a phoneme stream, asdescribed in U.S. patent application Ser. No. 11/513,269, entitled“Dynamic Speech Sharpening,” filed Aug. 31, 2006, which issued as U.S.Pat. No. 7,634,409 on Dec. 15, 2009, and which is hereby incorporated byreference in its entirety. The one or more preliminary interpretationsgenerated by the speech recognition engine 110 may then be provided to aconversational speech engine 115 for further processing. Conversationalspeech engine 115 may include a conversational language processor 120and/or a voice search engine 125, described in greater detail in FIG. 2below. Conversational speech engine 115 may communicate with one or moredatabases 130 to generate an adaptive conversational response, which maybe returned to the user as an output 140. In one implementation, output140 may be a multi-modal output and/or an interaction with one or moreapplications 145 to complete the request. For example, output 140 mayinclude a combination of an audible response and a display of a route ona navigation device. For example, the utterance may include a request toperform an action, and output 140 may include a conversational responsereporting success or failure, as well as an execution of the action. Inaddition, in various implementations, the speech recognition engine 110,conversational speech engine 115, and/or databases 130 may residelocally (e.g., on a user device), remotely (e.g., on a server), or ahybrid model of local and remote processing may be used (e.g.,lightweight applications may be processed locally while computationallyintensive applications may be processed remotely).

Referring to FIG. 2, an exemplary block diagram is provided illustratinga conversational speech engine 215 according to one aspect of theinvention. Conversational speech engine 215 may include a conversationallanguage processor 220 that generates an adaptive conversationalresponse to a request or series of requests using a free form voicesearch module 245, a noise tolerance module 250, and/or a contextdetermination process 255. According to one aspect of the invention,modules 245-255 may communicate with a voice search engine 225 thatincludes one or more context domain agents 230 and/or one or morevocabularies 235 to aid in interpreting utterances and generatingresponses, as described in “Enhancing the VUE™ (Voce-User-Experience)Through Conversational Speech,” by Tom Freeman and Larry Baldwin, whichis herein incorporated by reference in its entirety. Conversationalspeech engine 215 may generate an adaptive conversational response toone or more requests, where the requests may depend on unspokenassumptions, incomplete information, context established by previousutterances, user profiles, historical profiles, environmental profiles,or other information. Moreover, conversational speech engine 215 maytrack which requests have been completed, which requests are beingprocessed, and/or which requests cannot be processed due to incompleteor inaccurate information, and the response may be generatedaccordingly.

According to one aspect of the invention, free form voice search module245 may understand an utterance made using typical, day-to-day language(i.e., in free form), and may account for variations in how humansnormally speak, the vocabulary they use, and the conditions in whichthey speak. Because variables such as stress, distraction, andserendipity are always different and infinitely varied, free form searchmodule 245 may be designed with a goal of understanding that no humanwill come to the same Human-to-Machine interface situation in the sameway twice. Thus, free form search module 245 may implement one or morefeatures that model casual human speech. In various implementations,free form search module 245 may include, among other things, a free formutterance feature, a one-step access feature, an inferencing intendedoperations feature, an alternative expression feature, and/or animperfect speech feature.

The free form utterance feature may understand specialized jargon and/orslang, tolerate variations in word order (e.g., whether a subject of arequest comes before or after a verb may be irrelevant), and tolerateverbalized pauses (e.g., “um,” “ah,” “eh,” and other utterances withoutmeaning). For example, the free form utterance feature may treatformalized English verb-before-noun requests in an equivalent manner tofree form requests where a noun may precede a verb. For example, userutterances of “Change it to the Squizz” and “You know, urn, that Squizzchannel, ah, switch it there” may be treated equivalently (where Squizzis a channel on XM Satellite Radio). In either case, the free formutterance feature is able to identify “Squizz” as a subject of theutterance and “Change it” or “switch it” as a verb or request for theutterance (e.g., by cooperating with context determination process 255,or other features, and identifying a relevant context domain agent 230and/or vocabulary 235 to interpret the utterance).

The one-step access feature may understand utterances that includecompound requests with multiple variables. For example, a user utterancemay be “What is the forecast for Boston this weekend?” The one-stepaccess feature may identify “weather” as a context (e.g., by cooperatingwith context determination process 255, or other features, andidentifying “forecast” as a synonym of “weather”), and search for a cityequal to “Boston” and a time equal to “weekend.” By identifying allrelevant information for completing a task from a single utterance, theone-step access feature may overcome drawbacks of existing voice userinterfaces, such as Command and Control systems that use verbal menus torestrict information that a person can provide at a given point (e.g., aCommand and Control system for a phone directory service may say: “Stateplease,” . . . “City please,” . . . “What listing,” etc.). Moreover,some utterances may include compound requests, and the one-step accessfeature may decompose the compound requests into sub-tasks. For example,a user utterance of “I need to be at a meeting tomorrow in San Franciscoat 8:00 am” may be decomposed into a set of sub-tasks such as (1)checking availability and reserving a flight on an evening before themeeting, (2) checking availability and reserving a hotel, (3) checkingavailability and reserving a car, etc., where users may furtherdesignate preferences for various tasks (e.g., first check availabilityon an airline for which the user is a frequent flyer). Depending on alevel of shared knowledge about a user's preferences and/or historicalpatterns, the one-step access feature may infer additional tasks from arequest. For example, in the above example, the one-step access featuremay also check a weather forecast, and if the weather is “nice” (asdefined by the user preferences and/or as inferred from historicalpatterns), the one-step access feature may schedule a tee-time at apreferred golf course in San Francisco.

The inferencing intended operations feature may identify an intendedrequest from incomplete or ambiguous requests. For example, when a userutters “Route <indecipherable> Chicago <indecipherable> here,” where theuser intended to say “Route calculation to Chicago from here,” theinferencing intended operations feature may model what is required tocalculate a route (an origination point and a destination point).Because the utterance includes the origination point and the destinationpoint, a request to calculate a route from the user's present locationto Chicago may be inferred. Similarly, when the inferencing intendedoperations feature does not have sufficient information to infer acomplete request, an adaptive conversational response may be generatedto prompt the user for missing information. For example, when anutterance includes a request for a stock quote but not a company name(e.g., “Get me the stock price for <indecipherable>”), the response maybe “What company's stock quote do you want?” The user may then providean utterance including the company name, and the request may becompleted. In one implementation, the response may ask for missinginformation in a way that most restricts possible interpretations (e.g.,in a request for a task that requires both a city and a state, the statemay be asked for first because there are fewer states than cities).Moreover, the inferencing intended operations feature may model compoundtasks and/or requests by maintaining context and identifying relevantand/or missing information at both a composite and sub-task level.

The alternative expression feature may recognize common alternatives fornouns and verbs to reflect variations in usage patterns according tovarious criteria. For example, users may vary expression based on age,socio-economics, ethnicity, user whims, or other factors. Thus, thealternative expression feature may support variations in expressionwhere word order is unimportant or unanticipated. Alternatives inexpression based on various criteria or demographics may be loaded intocontext domain agents 230 and/or vocabularies 235, and the alternativeexpression feature may update context domain agents 230 and/orvocabularies 235 based on inferred or newly discovered variations. Inone implementation, conversational speech engine 215 may include asubscription interface to update changes to context domain agents 230and/or vocabularies 235 (e.g., a repository may aggregate various userutterances and deploy updates system wide). In operation, thealternative expression feature may allow nouns and/or verbs to berepresented in different ways to give simplistic, yet representative,examples. For example, a user interested in a weather forecast forWashington, D.C. may provide any of the following utterances, each ofwhich are interpreted equivalently: “What's the weather like in DC,” “Isit raining inside the Beltway,” Gimme the forecast for the capital,”etc. Similarly, utterances of “Go to my home,” “Go home,” “Show route tohome,” and “I would like to know my way home” may all be interpretedequivalently, where a user profile may include the user's home addressand a navigation route to the home address may be calculated.

The imperfect speech feature may be able to infer requests fromcontradictory or otherwise inaccurate information, such as when anutterance includes starts and stops, restarts, stutters, run-onsentences, or other imperfect speech. For example, a user may sometimeschange their mind, and thus alter the request in mid-utterance, and theimperfect speech feature may nonetheless be able to infer a requestbased on models of human speech. For example, for an utterance of “Well,I wanna . . . Mexi . . . no, steak restaurant please, I'm hungry,”existing voice user interfaces make no assumptions regarding models ofhuman speech and would be unable to infer whether the user wanted aMexican or steak restaurant. The imperfect speech feature overcomesthese drawbacks by using various models of human understanding that mayindicate that a last criterion is most likely to be correct, orintonation, emphasis, stress, use of the word “not,” or other models mayindicate which criterion is most likely to be correct. Thus, in theabove example, the imperfect speech feature may infer that the userwants a steak restaurant.

According to one aspect of the invention, noise tolerance module 250 maybe closely related to the imperfect speech feature, and may operate todiscard words or noise that has no meaning in a given context so as notto create confusion. Moreover, noise tolerance module 250 may filter outenvironmental and non-human noise to further reduce a likelihood ofconfusion. In one implementation, noise tolerance module 250 maycooperate with other modules and features to filter out words that donot fit into a context. For example, one or more contexts may beidentified, and words that have no meaning with respect to systemcapabilities, random human utterances without meaning and other noisemay be filtered out. Thus, noise tolerance module 250 may modelreal-world conditions to identify meaningful requests. For example,noise tolerance module 250 may filter other human conversations and/orutterances within a range of one or more microphones, For example, asingle device may include multiple microphones, or multiple devices mayeach include one or more microphones, and the noise tolerance module maycollate inputs and cooperatively filter out sound by comparing a speechsignal from the various microphones. Noise tolerance module 250 may alsofilter out non-human environmental noise within the range of themicrophones, out-of-vocabulary words, which could be a result of speakerambiguity or malapropisms, or other noise that may be unrelated to atarget request. Noise models in noise tolerance module 250 may defineperformance benchmarks based on human criteria. For example, if a driverof a car, traveling at 65 miles-per-hour, with windows cracked is 92%likely to be understood by a passenger, then noise tolerance module 250may have a similar performance under those conditions.

According to one aspect of the invention, conversational speech engine215 may include a context determination process 255 that determines oneor more contexts for a request to establish meaning within aconversation. The one or more contexts may be determined by having oneor more context domain agents compete to determine a most appropriatedomain for a given utterance, as described in U.S. patent applicationSer. No. 11/197,504, entitled “Systems and Methods for Responding toNatural Language Speech Utterance,” filed Aug. 5, 2005, which issued asU.S. Pat. No. 7,640,160 on Dec. 29, 2009 and U.S. patent applicationSer. No. 11/212,693, entitled “Mobile Systems and Methods of SupportingNatural Language Human-Machine Interactions,” filed Aug. 29, 2005, whichissued as U.S. Pat. No. 7,949,529 on May 24, 2011, both of which arehereby incorporated by reference in their entirety. Once a given contextdomain agent “wins” the competition, the winning agent may beresponsible for establishing or inferring further contexts and updatingshort-term and long-term shared knowledge. If there is a deadlockbetween context domain agents, an adaptive conversational response mayprompt the user to assist in disambiguating between the deadlockedagents. For example, a user utterance of “What about traffic?” may havea distinct meaning in various contexts. That is, “traffic” may have afirst meaning when the user is querying a system's media player (i.e.,“traffic” would be a Rock and Roll band led by singer/songwriter SteveWinwood), a second meaning when the user is querying a search interfaceregarding Michael Douglas films (i.e., “traffic” would be a filmdirected by Steven Soderbergh), a third meaning when the user isquerying a navigation device for directions to an airport (i.e.,“traffic” would be related to conditions on roads along a route to theairport).

Moreover, context determination process 255 may infer intendedoperations and/or context based on previous utterances and/or requests,whereas existing systems consider each utterance independently,potentially making the same errors over and over again. For example, ifa given interpretation turns out to be incorrect, the incorrectinterpretation may be removed as a potential interpretation from one ormore grammars associated with the speech recognition engine and/or frompossible subsequent interpretations determined by context determinationprocess 255, thereby assuring that a mistake will not be repeated for anidentical utterance.

Context determination process 255 may overcome drawbacks of existingsystems by continually updating one or more models of an existingcontext, where establishing context may be a by-product of aconversation, which cannot be established a priori. Contextdetermination process 255 may establish a first context domain, changeto a second context domain, change back to the first context domain, andso on, as tasks are completed, partially completed, requested, etc, anda context stack may track conversation topics and attempt to fit acurrent utterance into a most-recent context, next-most-recent topic,etc., traversing the context stack until a most likely intent can beestablished. For example, a user may utter “What's the traffic report,”and context determination process 255 may establish Traffic as acontext, and return an output including a traffic report, which does nothappen to mention traffic on Interstate-5. The user may then utter “Whatabout I-5?” and context determination process 255 may know that thecurrent context is Traffic, a traffic report including information aboutInterstate-5 may be searched for, and the traffic report indicating thatInterstate-5 is crowded may be returned as an output. The user may thenutter “Is there a faster way?” and context determination module 255 mayknow that the current context is still Traffic, and may search forroutes to a specified destination with light traffic and avoidingInterstate-5. Moreover, context determination process 255 may buildcontext based on user profiles, environmental profiles, historicalprofiles, or other information to further refine the context. Forexample, the profiles may indicate that Interstate-5 is a typical routetaken Monday through Friday.

The profiles may be particularly meaningful when attempting todisambiguate between contexts where a word has different meanings indifferent contexts. For example, a user may utter “What's the weather inSeattle?” and context determination process 255 may establish Weather asa context, as well as establishing Seattle as an environmental context.The user may then utter “and Portland?” and context determinationprocess 255 may return a weather report for Portland, Oreg. based on theWeather and an environmental proximity between Portland, Oreg. andSeattle, Wash. The user may then ask “What time does the game start?”and a search for sports events with teams from Seattle and/or Portlandmay occur, with results presented conversationally according to methodsdescribed in greater detail below in FIG. 3. Correlatively, had useroriginally uttered “What's the weather in Portsmouth, N.H.,” in thesecond utterance, context determination process 255 may instead retrievea weather report for Portland, Me. based on an environmental proximityto New Hampshire. Moreover, when environmental profiles, contextualshared knowledge, and/or other short-term and/or long-term sharedknowledge does not provide enough information to disambiguate betweenpossibilities, responses may prompt the user with a request for furtherinformation (e.g., “Did you mean Portland, Me., or Portland, Oreg.?”).

Context determination process 255 may cooperate with context domainagents 230, where each context domain agent 230 may define a collectionof related functions that may be useful for users. Moreover, eachcontext domain agent 230 may include a relevant vocabulary 235 andthought collections that model word groupings, which when evaluatedtogether, may disambiguate one context domain from another (e.g., aMusic context domain agent 230 may include a vocabulary 235 for songs,artists, albums, etc., whereas a Stock context domain agent 230 mayinclude a vocabulary 235 for company names, ticker symbols, financialmetrics, etc.). Thus, accuracy in identifying meaning may be enhanced byeliminating out-of-context words and noise words when searching forrelevant combinations. In contrast, existing systems attempt to assignmeaning to every component of an utterance (e.g., includingout-of-context words and noise words), which results in nearly infinitepossible combinations and greater likelihood of confusion. Moreover,context domain agents 230 may include metadata for each criteria tofurther assist in interpreting utterances, inferring intent, completingincomplete requests, etc. (e.g., a Space Needle vocabulary word mayinclude metadata for Seattle, landmark, tourism, Sky City restaurant,etc.). Given a disambiguated criterion, context determination process255 may thus be able to automatically determine other information neededto complete a request, discard importance of word order, and performother enhancements for conversational speech.

Context domain agents 230 may also be self-aware, assigning degrees ofcertainty to one or more generated hypotheses, where a hypothesis may bedeveloped to account for variations in environmental conditions, speakerambiguity, accents, or other factors. Conceptually, context domainagents 230 may be designed to model utterances as a hard-of-hearingperson would at a noisy party. By identifying a context, capabilitieswithin the context, vocabularies within the context, what tasks are donemost often historically in the context, what task was just completed,etc., a context domain agent 230 may establish intent from rather meagerphonetic clues. Moreover, the context stack may be one of a plurality ofcomponents for establishing context, and thus not a constraint upon theuser. All context domains may be accessible, allowing the user to switchcontexts at any time without confusion. Thus, just as in human-to-humanconversation, context domains may be rapidly selected, withoutmenu-driven dead ends, when an utterance is unambiguous. For example, auser may utter, “Please call Rich Kennewick on his cell phone,” and asystem response of “Do you wish me to call Rich Kennewick on his cell?”may be generated. The user may decide at that point to call RichKennewick later, and instead, listen to some music. Thus, the user maythen utter, “No, play the Louis Armstrong version of Body and Soul frommy iPod,” and a system response of “Playing Body and Soul by LouisArmstrong” may be generated as Body and Soul is played through a mediaplayer. In this example, the later utterance has no contextualconnection to the first utterance, yet because request criteria in theutterances are unambiguous, contexts can be switched easily withoutrelying on the context stack.

Referring to FIG. 3, an exemplary cooperative conversational model 300is illustrated according to an aspect of the invention. Cooperativeconversational model 300 may build upon free form voice search 245,noise tolerance 250, and context determination 255 to implement aconversational Human-to-Machine interface that reflects how humansinteract with each other and their normal behavior in conversation.Simply put, cooperative conversational model 300 enables humans andmachines to participate in a conversation with an accepted purpose ordirection, with each participant contributing to the conversation forthe benefit of the conversation. That is, cooperative conversationalmodel 300 incorporates technology and process-flow that takes advantageof human presumptions about utterances that humans rely upon, both asspeakers and listeners, thereby creating a Human-to-Machine interfacethat is analogous to everyday human-to-human conversation. In oneimplementation, a cooperative conversation may take incoming data(shared knowledge) 305 to inform a decision (intelligent hypothesisbuilding) 310, and then may refine the decision and generate a response(adaptive response building) 315.

According to one aspect of the invention, shared knowledge 305 includesboth short-term and long-term knowledge about incoming data. Short-termknowledge may accumulate during a single conversation, while long-termknowledge may accumulate over time to build user profiles, environmentalprofiles, historical profiles, cognitive profiles, etc.

Input received during a single conversation may be retained in a SessionInput Accumulator. The Session Input Accumulator may includecross-modality awareness, where in addition to accumulating inputrelating to user utterances, requests, locations, etc., the SessionInput Accumulator may accumulate a current user interface state relatingto other modal inputs to further build shared knowledge models and moreaccurate adaptive responses (e.g., when a user utters a request relatingto a portion of a touch-screen device, as described above). For example,the Session Input Accumulator may accumulate inputs includingrecognition text for each utterance, a recorded speech file for eachutterance, a list-item selection history, a graphical user interfacemanipulation history, or other input data. Thus, the Session InputAccumulator may populate Intelligent Hypothesis Builder 310 with currentand relevant information, build long-term shared knowledge byidentifying information with long-term significance, provide AdaptiveResponse Builder 315 with relevant state and word usage information,retain recent contexts for use with Intelligent Hypothesis Builder 310,and/or retain utterances for reprocessing during multi-pass evaluations.Moreover, because cooperative conversations 300 model humanconversations, short-term session data may be expired after apsychologically appropriate amount of time, thereby humanizing systembehavior. For example, a human is unlikely to recall a context of aconversation from two years ago, but because the context would beidentifiable by a machine, session context is expired after apredetermined amount of time to reduce a likelihood of contextualconfusion based on stale data. However, relevant information from anexpired session context may nonetheless be added to user, historical,environmental, cognitive, or other long-term knowledge models.

Long-term shared knowledge may generally be user-centric, rather thansession-based. That is, inputs may be accumulated over time to builduser, environmental, cognitive, historical, or other long-term knowledgemodels. Long-term and short-term shared knowledge (collectively, sharedknowledge 305) may be used simultaneously anytime a user engages in acooperative conversation 300. Long-term shared knowledge may includeexplicit and/or implicit user preferences, a history of most recentlyused agents, contexts, requests, tasks, etc., user-specific jargonrelated to vocabularies and/or capabilities of an agent and/or context,most often used word choices, or other information. The long-term sharedknowledge may be used to populate Intelligent Hypothesis Builder 310with current and relevant information, provide Adaptive Response Builder315 with appropriate word choices when the appropriate word choices areunavailable via the Session Input Accumulator, refine long-term sharedknowledge models, identify a frequency of specific tasks, identify tasksa user frequently has difficulty with, or provide other informationand/or analysis to generate more accurate conversational responses.

As described above, shared knowledge 305 may be used to populateIntelligent Hypothesis Builder 310, such that a user and a voice userinterface may share assumptions and expectations such as topicknowledge, conversation history, word usage, jargon, tone (e.g., formal,humorous, terse, etc.), or other assumptions and/or expectations thatfacilitate interaction at a Human-to-Machine interface.

According to an aspect of the invention, one component of a successfulcooperative conversation may be identifying a type of conversation froman utterance. By categorizing and developing conceptual models forvarious types of exchanges, user expectations and domain capabilitiesmay be consistently aligned. Intelligent Hypothesis Builder 310 maygenerate a hypothesis as to a conversation type by consideringconversational goals, participant roles, and/or an allocation ofinformation among the participants. Conversational goals may broadlyinclude: (1) getting a discrete piece of information or performing adiscrete task, (2) gathering related pieces of information to make adecision, and/or (3) disseminating or gathering large amounts ofinformation to build expertise. Participant roles may broadly include:(1) a leader that controls a conversation, (2) a supporter that followsthe leader and provides input as requested, and/or (3) a consumer thatuses information. Information may be held by one or more of theparticipants at the outset of a conversation, where a participant mayhold most (or all) of the information, little (or none) of theinformation, or the information may be allocated roughly equally amongstthe participants. Based on the conversational goals, participant roles,and allocation of information, Intelligent Hypothesis Builder 310 mayconsider various factors to classify a conversation (or utterance) intogeneral types of conversations that can interact with one another toform many more variations and permutations of conversation types (e.g.,a conversation type may change dynamically as information is reallocatedfrom one participant to another, or as conversational goals change basedon the reallocation of information).

For example, in one implementation, a query conversation may include aconversational goal of getting a discrete piece of information orperforming a particular task, where a leader of the query conversationmay have a specific goal in mind and may lead the conversation towardachieving the goal. The other participant may hold the information andmay support the leader by providing the information. In a didacticconversation, a leader of the conversation may control informationdesired by a supporter of the conversation. The supporter's role may belimited to regulating an overall progression of the conversation andinterjecting queries for clarification. In an exploratory conversation,both participants share leader and supporter roles, and the conversationmay have no specific goal, or the goal may be improvised as theconversation progresses. Based on this model, Intelligent HypothesisBuilder 310 may broadly categorize a conversation (or utterance)according to the following diagram:

QUERY Participant A Participant B User Voice User Interface GOAL Getinformation/action Provide information/action ROLE Leader/ConsumerSupporter/Dispenser INFORMATION Less More ALLOCATION

DIDACTIC Participant A Participant B User Voice User Interface GOAL Getinformation Provide information ROLE Follower/Consumer Leader/DispenserINFORMATION Less More ALLOCATION 

EXPLORATORY Participant A Participant B User Voice User Interface GOALGather/share information Gather/share information ROLE Follower/Consumerand Follower/Consumer and Leader/Dispenser Leader/Dispenser INFORMATIONEqual or alternating Equal or alternating ALLOCATION

Intelligent Hypothesis Builder 310 may use an identified conversationtype to assist in generating a set of hypotheses as to a user's intentin an utterance. In addition, Intelligent Hypothesis Builder 310 may useshort-term shared knowledge from the Session Input Accumulator toproactively build and evaluate interaction with a user as a conversationprogresses, as well as long-term shared knowledge to proactively buildand evaluate interaction with the user over time. Intelligent HypothesisBuilder 310 may thus adaptively arrive at a set of N-best hypothesesabout user intent, and the N-best hypotheses may be provided to anAdaptive Response Builder 315. In addition, Intelligent HypothesisBuilder 310 may model human-to-human interaction by calculating a degreeof certainty for each of the hypotheses. That is, just as humans rely onknowledge shared by participants to examine how much and what kind ofinformation was available, Intelligent Hypothesis Builder 310 mayleverage the identified conversation type and short-term and long-termshared knowledge to generate a degree of certainty for each hypothesis.

According to another aspect of the invention, Intelligent HypothesisBuilder 310 may generate one or more explicit hypotheses of a user'sintent when an utterance contains all information (including qualifiers)needed to complete a request or task. Each hypothesis may have acorresponding degree of certainty, which may be used to determine alevel of unprompted support to provide in a response. For example, aresponse may include a confirmation to ensure the utterance was notmisunderstood or the response may adaptively prompt a user to providemissing information.

According to another aspect of the invention, Intelligent HypothesisBuilder 310 may use short-term knowledge to generate one or moreimplicit hypotheses of a user's intent when an utterance may be missingrequired qualifiers or other information needed to complete a request ortask. Each hypothesis may have a corresponding degree of certainty. Forinstance, when a conversation begins, short-term knowledge stored in theSession Input Accumulator may be empty, and as the conversationprogresses, the Session Input Accumulator may build a history of theconversation. Intelligent Hypothesis Builder 310 may use data in theSession Input Accumulator to supplement or infer additional informationabout a current utterance. For example, Intelligent Hypothesis Builder310 may evaluate a degree of certainty based on a number of previousrequests relevant to the current utterance. In another example, when thecurrent utterance contains insufficient information to complete arequest or task, data in the Session Input Accumulator may be used toinfer missing information so that a hypothesis can be generated. Instill another example, Intelligent Hypothesis Builder 310 may identifysyntax and/or grammar to be used by Adaptive Response Builder 315 toformulate personalized and conversational response. In yet anotherexample, when the current utterance contains a threshold amount ofinformation needed to complete a request or task, data in the SessionInput Accumulator may be relied upon to tune a degree of certainty.

According to another aspect of the invention, Intelligent HypothesisBuilder 310 may use long-term shared knowledge to generate one or moreimplicit hypotheses of a user's intent when an utterance is missingqualifiers or other information needed to complete a request or task.Each hypothesis may have a corresponding degree of certainty. Usinglong-term knowledge may be substantially similar to using short-termshared knowledge, except that information may be unconstrained by acurrent session, and an input mechanism may include information fromadditional sources other than conversational sessions. For example,Intelligent Hypothesis Builder 310 may use information from long-termshared knowledge at any time, even when a new conversation is initiated,whereas short-term shared knowledge may be limited to an existingconversation (where no short-term shared knowledge would be availablewhen a new conversation is initiated). Long-term shared knowledge maycome from several sources, including user preferences or a plug-in datasource (e.g., a subscription interface to a remote database), expertiseof a user (e.g., based on a frequency of errors, types of tasksrequested, etc., the user may be identified as a novice, intermediate,experienced, or other type of user), agent-specific information and/orlanguage that may also apply to other agents (e.g., by decouplinginformation from an agent to incorporate the information into otheragents), frequently used topics passed in from the Session InputAccumulator, frequently used verbs, nouns, or other parts of speech,and/or other syntax information passed in from the Session InputAccumulator, or other sources of long-term shared knowledge may be used.

According to another aspect of the invention, knowledge-enabledutterances, as generated by Intelligent Hypothesis Builder 310, mayinclude one or more explicit (supplied by a user), and one or moreimplicit (supplied by Intelligent Hypothesis Builder 310) contextualsignifiers, qualifiers, criteria, and other information that can be usedto identify and evaluate relevant tasks. At that point, IntelligentHypothesis Builder 310 may provide an input to Adaptive Response Builder315. The input received by Adaptive Response Builder 315 may include atleast a ranked list of hypotheses, including explicit and/or implicithypotheses, each of which may have a corresponding degree of certainty.A hypothesis may be assigned one of four degrees of certainty: (1)“sure,” where contextual signifiers and qualifiers relate to one task,context and qualifiers relate to one task, and a confidence levelassociated with a preliminary interpretation generated at the speechrecognition engine exceeds a predetermined threshold; (2) “pretty sure,”where contextual signifiers and qualifiers relate to more than one task(select top-ranked task) and criteria relates to one request, and/or theconfidence level associated with the preliminary interpretationgenerated at the speech recognition engine is below the predeterminedthreshold; (3) “not sure,” where additional contextual signifiers orqualifiers are needed to indicate or rank a task; and (4) “nohypothesis,” where little or no information can be deciphered. Eachdegree of certainty may further be classified as explicit or implicit,which may be used to adjust a response. The input received by AdaptiveResponse Builder 310 may also include a context, user syntax and/orgrammar, context domain agent specific information and/or preferences(e.g., a travel context domain agent may know a user frequently requestsinformation about France, which may be shared with a movie contextdomain agent so that responses may occasionally include French movies).

According to another aspect of the invention, Adaptive Response Builder315 may build syntactically, grammatically, and contextually sensitive“intelligent responses” that can be used with one or more agents togenerate a conversational experience for a user, while also guiding theuser to reply in a manner favorable for recognition. In oneimplementation, the intelligent responses may include a verbal oraudible reply played through an output device (e.g., a speaker), and/oran action performed by a device, computer, or machine (e.g., downloadinga web page, showing a list, executing an application, etc.). In oneimplementation, an appropriate response may not require conversationaladaptation, and default replies and/or randomly selected response setsfor a given task may be used.

According to another aspect of the invention, Adaptive Response Builder310 may draw on information maintained by Intelligence HypothesisBuilder 310 to generate responses that may be sensitive to context, taskrecognition of a current utterance, what a user already knows about atopic, what an application already knows about the topic, sharedknowledge regarding user preferences and/or related topics, appropriatecontextual word usage (e.g., jargon), words uttered by the user inrecent utterances, conversational development and/or course correction,conversational tone, type of conversation, natural variation in wordingof responses, or other information. As a result, Adaptive ResponseBuilder 315 may generate intelligent responses that createconversational feel, adapt to information that accumulates over aduration of a conversation, maintain cross-modal awareness, and keep theconversation on course.

According to another aspect of the invention, Adaptive Response Builder315 may create a conversational feel by adapting to a user's manner ofspeaking, framing responses appropriately, and having natural variationand/or personality (e.g., by varying tone, pace, timing, inflection,word use, jargon, and other variables in a verbal or audible response).Adapting to a user's manner of speaking may include using contextualsignifiers and grammatical rules to generate one or more sentences foruse as response sets that may cooperate with the user. By takingadvantage of short-term (from the Session Input Accumulator) andlong-term (from one or more profiles) shared knowledge about how a userutters a request, the responses may be modeled using techniques used torecognize requests. Adaptive Response Builder 315 may rate possibleresponses statistically and/or randomize responses, which creates anopportunity to build an exchange with natural variation andconversational feel. This may be a significant advantage over existingvoice user interfaces with incongruous input and output, where the inputis “conversational” and the output is “computerese.” The followingexamples may demonstrate how a response may adapt to a user's input wordchoices and manner of speaking:

User Do you know [mumbled words] Seattle [more mumbled words]? VoiceUser Did you want Seattle sports scores, weather, traffic, or Interfacenews?

User Find me [mumbled words] Seattle [more mumbled words]? Voice User Ifound Seattle, did you want sports scores, weather, traffic, Interfaceor news?

User Get me [mumbled words] Seattle [more mumbled words]? Voice UserI've got Seattle, did you want me to get sports scores, Interfaceweather, traffic, or news?

According to another aspect of the invention, Adaptive Response Builder315 may frame responses to influence a user to reply with an utterancethat may be easily recognized. For example, a user may utter, “Get methe news” and a voice user interface response may be “Which of thesecategories? Top news stories, international news, political news, orsports news?” The response may be likely to illicit utterances from theuser, such as “Top news stories” or “International news,” which are morelikely to result in a completed request. Thus, the responses may conformto a cooperative nature of human dialog, and a natural human tendency to“parrot” what was just heard as part of a next utterance. Moreover,knowledge of current context may enhance responses to generate moremeaningful conversational responses, such as in the following exchange:

User What's the weather like in Dallas? Voice User In Dallas, it's sunnyand 90 degrees. Interface User What theaters are showing the movie “TheFantastic Four” there? Voice User 10 theaters in Dallas are showing “TheFantastic Four.” Interface Do you want show times for a particulartheater?

Framing the responses may also deal with misrecognitions according tohuman models. For example, humans frequently remember a number of recentutterances, especially when one or more previous utterances weremisrecognized or unrecognized. Another participant in the conversationmay limit correction to a part of the utterance that was misrecognizedor unrecognized, or over subsequent utterances and/or otherinteractions, clues may be provided to indicate the initialinterpretation was incorrect. Thus, by storing and analyzing multipleutterances, utterances from earlier in a conversation may be correctedas the conversation progresses.

According to another aspect of the invention, Adaptive Response Builder315 may generate multi-modal, or cross-modal, responses to a user. Inone implementation, responses may be aware of and control one or moredevices and/or interfaces, and users may respond by using whicheverinput method, or combination of input methods, is most convenient. Forexample, a response asking the user to direct an utterance with a “Yes”or “No” in a multi-modal environment may also display alternativesvisually.

According to another aspect of the invention, Adaptive Response Builder315 may correct a course of a conversation without interruptingconversational flow. Adaptive Response Builder 315 may generateintelligent responses based on the ranked list of hypotheses andcorresponding degrees of certainty, which may be used to correct acourse of a conversation without interrupting conversational flow. Thatis, even though the intelligent responses may be reasonably “sure,” theintelligent responses may nonetheless sometimes be incorrect. Whileexisting voice user interfaces tend to fail on average conversationalmissteps, normal human interactions may expect missteps and deal withthem appropriately. Thus, responses after a misrecognition may bemodeled after clarifications, rather than errors, and words may chosenin subsequent responses that move conversation forward and establish anappropriate domain to be explored with the user. For example, coursecorrection may result in the following exchange:

User Can you get [mumbled words here] Mariners [more mumbled words]?Voice User Interface I've got the score for the Mariners game? 4-2Mariners. User No, the start time for tomorrow's game. Voice UserInterface Oh, 7:05.

The above disclosure has been described in terms of specific exemplaryaspects, implementations, and embodiments of the invention. However,those skilled in the art will recognize various changes andmodifications that may be made without departing from the scope andspirit of the invention. Therefore, the specification and drawings areto be regarded as exemplary only, and the scope of the invention is tobe determined solely by the appended claims.

What is claimed is:
 1. A computer-implemented method of facilitatingconversation-based responses, the method being implemented by a computersystem that includes one or more physical processors executing one ormore computer program instructions which, when executed, perform themethod, the method comprising: receiving, at the computer system, anatural language utterance during a conversation between a user and thecomputer system; identifying, by the computer system, a first model thatincludes short-term knowledge about the conversation, wherein theshort-term knowledge is based on one or more prior natural languageutterances received during the conversation; identifying, by thecomputer system, based on the short-term knowledge, context informationfor the natural language utterance; determining, by the computer system,based on the context information, an interpretation of the naturallanguage utterance; and generating, by the computer system, based on theinterpretation of the natural language utterance, a response to thenatural language utterance.
 2. The method of claim 1, whereindetermining the interpretation of the natural language utterancecomprises determining, based on the context information, aninterpretation of one or more recognized words of the natural languageutterance.
 3. The method of claim 1, further comprising: updating, bythe computer system, based on information about the natural languageutterance, during the conversation, the short-term knowledge in thefirst model, wherein the updated short-term knowledge is used todetermine subsequent context information for one or more subsequentnatural language utterances received during the conversation.
 4. Themethod of claim 1, further comprising: identifying, by the computersystem, a second model that includes long-term knowledge about one ormore prior conversations between the user and the computer system,wherein determining the context information comprises determining, basedon the short-term knowledge and the long-term knowledge, the contextinformation.
 5. The method of claim 4, further comprising: updating, bythe computer system, based on information about the natural languageutterance, the long-term knowledge in the second model, wherein theupdated long-term knowledge is used to determine subsequent contextinformation for one or more subsequent natural language utterancesreceived during the conversation.
 6. The method of claim 5, wherein theupdated long-term knowledge is used to determine subsequent contextinformation for one or more subsequent conversations.
 7. The method ofclaim 1, further comprising: identifying, by the computer system, basedon the short-term knowledge, a manner in which the natural languageutterance is spoken, wherein generating the response comprisesgenerating, based on the identified manner and the interpretation of thenatural language utterance, the response.
 8. The method of claim 7,further comprising: identifying, by the computer system, a second modelthat includes long-term knowledge about one or more prior conversationsbetween the user and the computer system, wherein identifying the mannercomprises identifying, based on the short-term knowledge and thelong-term knowledge, the manner.
 9. The method of claim 7, whereingenerating the response comprises generating, based on a response formatassociated with the identified manner, the response.
 10. The method ofclaim 1, wherein the natural language utterance and the one or moreprior natural language utterances are associated with the user.
 11. Asystem for facilitating conversation-based responses, the systemcomprising: one or more physical processors programmed with one or morecomputer program instructions such that, when executed, the one or morecomputer program instructions cause the one or more physical processorsto: receive a natural language utterance during a conversation between auser and the system; identify a first model that includes short-termknowledge about the conversation, wherein the short-term knowledge isbased on one or more prior natural language utterances received duringthe conversation; identify, based on the short-term knowledge, contextinformation for the natural language utterance; determine, based on thecontext information, an interpretation of the natural languageutterance; and generate, based on the interpretation of the naturallanguage utterance, a response to the natural language utterance. 12.The system of claim 11, wherein determining the interpretation of thenatural language utterance comprises determining, based on the contextinformation, an interpretation of one or more recognized words of thenatural language utterance.
 13. The system of claim 11, wherein the oneor more physical processors are caused to: update, based on informationabout the natural language utterance, during the conversation, theshort-term knowledge in the first model, wherein the updated short-termknowledge is used to determine subsequent context information for one ormore subsequent natural language utterances received during theconversation.
 14. The system of claim 11, wherein the one or morephysical processors are caused to: identify a second model that includeslong-term knowledge about one or more prior conversations between theuser and the system, wherein determining the context informationcomprises determining, based on the short-term knowledge and thelong-term knowledge, the context information.
 15. The system of claim14, wherein the one or more physical processors are caused to: update,based on information about the natural language utterance, the long-termknowledge in the second model, wherein the updated long-term knowledgeis used to determine subsequent context information for one or moresubsequent natural language utterances received during the conversation.16. The system of claim 15, wherein the updated long-term knowledge isused to determine subsequent context information for one or moresubsequent conversations.
 17. The system of claim 11, wherein the one ormore physical processors are caused to: identify, based on theshort-term knowledge, a manner in which the natural language utteranceis spoken, wherein generating the response comprises generating, basedon the identified manner and the interpretation of the natural languageutterance, the response.
 18. The system of claim 17, wherein the one ormore physical processors are caused to: identify a second model thatincludes long-term knowledge about one or more prior conversationsbetween the user and the computer system, wherein identifying the mannercomprises identifying, based on the short-term knowledge and thelong-term knowledge, the manner.
 19. The system of claim 17, whereingenerating the response comprises generating, based on a response formatassociated with the identified manner, the response.
 20. The system ofclaim 11, wherein the natural language utterance and the one or moreprior natural language utterances are associated with the user.